An adaptive weighted least square support vector regression for hysteresis in piezoelectric actuators

© 2017 Elsevier B.V. To overcome the low positioning accuracy of piezoelectric actuators (PZAs) caused by the hysteresis nonlinearity, this paper proposes an adaptive weighted least squares support vector regression (AWLSSVR) to model the rate-dependent hysteresis of PZA. Firstly, the AWLSSVR hyperparameters are optimized by using particle swarm optimization. Then an adaptive weighting strategy is proposed to eliminate the effects of noises in the training dataset and reduce the sample size at the same time. Finally, the proposed approach is applied to predict the hysteresis of PZA. The results show that the proposed method is more accurate than other versions of least squares support vector regression for training samples with noises, and meanwhile reduces the sample size and speeds up calculation

Model predictive current control of switched reluctance motor with inductance auto-calibration

The thesis is composed of three papers, which investigate the application of Model Predictive Controller (MPC) for current control of Switched Reluctance Motor (SRM). Since the conventional hysteresis current control method is not suitable for high power SRM drive system with low inductance and limited switching frequency, MPC is a promising alternative approach for this application. The proposed MPC can cope with the measurement noise as well as uncertainties within the machine inductance profile. In the first paper, a MPC current control method for Double-Stator Switched Reluctance Motor (DSSRM) drives is presented. A direct adaptive estimator is incorporated to follow the inductance variations in a DSSRM. In the second paper, the Linear Quadratic (LQ) form and dynamic programming recursion for MPC are analyzed, afterwards the unconstrained MPC solution for stochastic SRM model is derived. The Kalman filter is employed to reduce the variance of measurement noises. Based on Recursive Linear-Square (RLS) estimation, the inductance profile is calibrated dynamically. In the third paper, a simplified recursive MPC current control algorithm for SRM is applied for embedded implementation. A novel auto-calibration method for inductance surface estimation is developed to improve current control performance of SRM drive in statistic terms. --Abstract, page iv

Inclusion of hysteresis and eddy current losses in dynamic induction machine models

This paper proposes a method for including both hysteresis losses and eddy current losses in the dynamic space vector model of induction machines. The losses caused by the rotation and magnitude changes of the flux vector are taken into account. The model can be applied, for example, to time-domain simulations and real-time applications such as drive control. Finite element analysis, simulations, and laboratory experiments of a 45-kW motor are used for the investigation. It is shown that the model can predict the iron losses in a wide frequency range. The accuracy is significantly improved as compared to earlier models.Peer reviewe

Identification of the Servomechanism used for micro-displacement

Friction causes important errors in the control of small servomechanism and should be determined with precision in order to increase the system performance. This paper describes the method to identify the model parameters of a small linear drive with ball-screw. Two kinds of friction models will be applied for the servomechanism looking to rise its micropositioning abilities. The first one includes the static, viscous and Stribeck friction with hysteresis, and the second one uses the Lugre model. The results will be compared taking into account the criterion error, the accuracy and the normalized mean-square-error of the identified mechanical parameters. The coefficients of the models are identified by a recursive identification method using data acquisition and special filtering technics. The least square identification method is used in this paper in order to establish the motor parameters used as initial condition of the recursive estimation method. Computer simulations and experimental results demonstrate the efficiency of the proposed model

Artificial intelligent based friction modelling and compensation in motion control system

The interest in the study of friction in control engineering has been driven by the need for 10 precise motion control in most of industrial applications such as machine tools, robot 11 systems, semiconductor manufacturing systems and Mechatronics systems. Friction has 12 been experimentally shown to be a major factor in performance degradation in various 13 control tasks. Among the prominent effects of friction in motion control are: steady state 14 error to a reference command, slow response, periodic process of sticking and sliding (stick-15 slip) motion, as well as periodic oscillations about a reference point known as hunting when 16 an integral control is employed in the control scheme. Table 1 shows the effects and type of 17 friction as highlighted by Armstrong et. al.(1994). It is observed that, each of task is 18 dominated by at least one friction effect ranging from stiction, or/and kinetic to negative 19 friction (Stribeck). Hence, the need for accurate compensation of friction has become 20 important in high precision motion control. Several techniques to alleviate the effects of 21 friction have been reported in the literature (Dupont and Armstrong, 1993; Wahyudi, 2003; 22 Tjahjowidodo, 2004; Canudas, et. al., 1986). 23 One of the successful methods is the well-known model-based friction compensation 24 (Armstrong et al., 1994; Canudas de Wit et al., 1995 and Wen-Fang, 2007). In this method, 25 the effect of the friction is cancelled by applying additional control signal which generates a 26 torque/force. The generated torque/force has the same value (or approximately the same) 27 with the friction torque/force but in opposite direction

From model-driven to data-driven : a review of hysteresis modeling in structural and mechanical systems

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section

Control of switched reluctance machines

This thesis is concerned with the control of switched reluctance machines for both motoring and generating applications. There are different control objectives in each case. For motoring operation, there are two possible control objectives. If the SRM is being employed in a servo-type application, the desire is for a constant output torque. However, for low performance applications where some amount of torque ripple is acceptable, the aim is to achieve efficient and accurate speed regulation. When the SRM is employed for generating purposes, the goal is to maintain the dc bus voltage at the required value while achieving maximum efficiency. Preliminary investigative work on switched reluctance machine control in both motoring and generating modes is performed. This includes the implementation and testing through simulation of two control strategies described in the literature. In addition, an experimental system is built for the development and testing of new control strategies. The inherent nonlinearity of the switched reluctance machine results in ripple in the torque profile. This adversely affects motoring performance for servo-type applications. Hence, three neuro-fuzzy control strategies for torque ripple minimisation in switched reluctance motors are developed. For all three control strategies, the training of a neurofuzzy compensator and the incorporation of the trained compensator into the overall switched reluctance drive are described. The performance of the control strategies in reducing the torque ripple is examined with simulations and through experimental testing. While the torque ripple is troublesome for servo-type applications, there are some applications where a certain amount of torque ripple is acceptable. Therefore, four simple motor control strategies for torque ripple-tolerant applications are described and tested experimentally. Three of the control strategies are for low speed motoring operation while the fourth is aimed at high speed motoring operation. Finally, three closed-loop generator control strategies aimed at high speed operation in single pulse mode are developed. The three control strategies are examined by testing on the experimental system. A comparison of the performance of the control strategies in terms of efficiency and peak current produced by each is presented

Control of an over-actuated nanopositioning system by means of control allocation

This Master’s Thesis is devoted to the analysis and design of a control structure for the nanopositioning system LAU based on the dynamic control allocation technique. The objective is to control the vertical displacement with nanometer precision under a control effort distribution criterion among the actuator set. In this case, the pneumatic actuator is used as a passive gravity compensator while the voice coil motor generates the transient forces. The analysis of the system characteristics allows defining the design criterion for the control allocation. In this direction, the proposed dynamic control allocation stage considers a frequency distribution of the control effort. The lower frequency components are assigned to the pneumatic actuator while the higher frequencies are handled by the voice coil drive. The significant actuator dynamics are compensated through a Kalman filter approach. The position controller is based on a feedback linearization framework with a disturbance observer for enhanced robustness. The experimental validation demonstrates the feasibility of the proposed technique.Diese Masterarbeit widmet sich der Analyse und dem Entwurf einer Regelungsstruktur für das Nanopositioniersystem LAU. Dabei werden Methoden untersucht, welche das notwendige Stellsignal auf zwei Aktoren aufteilen. Ziel ist es, die vertikale Verschiebung des LAU mit Nanometerpräzision zu regeln. In diesem Fall wird der pneumatische Aktor als passiver Schwerkraftkompensator verwendet, während die elktromagnetische Tauchspule die transienten Kräfte erzeugt. Die Analyse der Eigenschaften des LAUSystems ermöglicht die Definition der Entwurfskriterien zur Aufteilung der Stellgröße. In dieser Richtung berücksichtigt die vorgeschlagene dynamische Methode eine Aufteilung der Stellgröße bezüglich der Frequenzanteile. Die niederfrequenten Komponenten werden dem pneumatischen Aktor zugeordnet. Dem elektromagnetische Aktor werden die verbliebenen hochfrequenten Anteile zugeordnet. Die signifikanten Effekte der Aktordynamik in Bezug auf die Bewegungsdynamik werden durch einen Kalman- Filteransatz kompensiert. Nichtlineare Streckenanteile werden basierend auf dem Modell und einem Störbeobachter kompensiert, sodass der verbleibende Anteil des Positionsreglers mit linearen Methoden entworfen werden kann. Die experimentelle Validierung zeigt die Effektivität des untersuchten Konzeptes.Tesi